Machine Learning - Sistem Pembelajaran Otomatis

// ML-powered features dalam React applications
import { useEffect, useState } from 'react';

function SmartRecommendations({ userId }) {
    const [recommendations, setRecommendations] = useState([]);
    const [isLoading, setIsLoading] = useState(true);
    
    useEffect(() => {
        // ML API call untuk personalized recommendations
        fetch(`/api/ml/recommendations/${userId}`)
            .then(response => response.json())
            .then(data => {
                setRecommendations(data.predictions);
                setIsLoading(false);
            });
    }, [userId]);
    
    return (
        <div>
            {isLoading ? (
                <div>Analyzing your preferences...</div>
            ) : (
                <div>
                    {recommendations.map(item => (
                        <RecommendationCard 
                            key={item.id} 
                            item={item}
                            confidence={item.mlScore}
                        />
                    ))}
                </div>
            )}
        </div>
    );
}

// ML-enhanced performance optimization
class MLPerformanceOptimizer {
    constructor() {
        this.userBehaviorData = [];
        this.predictionModel = null;
    }
    
    // Collect user interaction data
    trackUserBehavior(action, context) {
        this.userBehaviorData.push({
            action,
            context,
            timestamp: Date.now(),
            performance: this.getCurrentPerformanceMetrics()
        });
        
        // Trigger ML analysis when enough data collected
        if (this.userBehaviorData.length > 100) {
            this.optimizeBasedOnPredictions();
        }
    }
    
    // Use ML to predict optimal loading strategies
    async optimizeBasedOnPredictions() {
        const predictions = await this.predictUserNeeds();
        
        // Implement [[Lazy Loading]] based on predictions
        if (predictions.likelyToScroll) {
            this.preloadBelowFoldContent();
        }
        
        // Optimize [[Performance Optimization]] strategies
        if (predictions.deviceCapabilities.isLowEnd) {
            this.enableLightweightMode();
        }
    }
}

// TensorFlow.js untuk browser-based ML
import * as tf from '@tensorflow/tfjs';

class EdgeMLProcessor {
    constructor() {
        this.model = null;
        this.isModelLoaded = false;
    }
    
    async loadModel() {
        try {
            // Load pre-trained model
            this.model = await tf.loadLayersModel('/models/user-intent-classifier.json');
            this.isModelLoaded = true;
            console.log('ML model loaded successfully');
        } catch (error) {
            console.error('Failed to load ML model:', error);
        }
    }
    
    // Real-time prediction tanpa server roundtrip
    async predictUserIntent(inputData) {
        if (!this.isModelLoaded) {
            await this.loadModel();
        }
        
        const prediction = this.model.predict(
            tf.tensor2d([inputData])
        );
        
        const result = await prediction.data();
        return {
            intent: this.mapPredictionToIntent(result),
            confidence: Math.max(...result)
        };
    }
    
    // Optimize UI berdasarkan predicted intent
    async optimizeInterface(userInput) {
        const prediction = await this.predictUserIntent(userInput);
        
        if (prediction.intent === 'search' && prediction.confidence > 0.8) {
            // Pre-load search components
            import('./SearchInterface').then(module => {
                // Dynamic import based on ML prediction
            });
        }
    }
}

// AI-assisted code optimization
class MLCodeOptimizer {
    constructor() {
        this.performanceHistory = [];
        this.optimizationModel = null;
    }
    
    // Analyze code patterns dan suggest optimizations
    async analyzeCodePerformance(codeSnippet) {
        const features = this.extractCodeFeatures(codeSnippet);
        const prediction = await this.optimizationModel.predict(features);
        
        return {
            performanceScore: prediction.score,
            suggestions: prediction.optimizations,
            estimatedImprovement: prediction.improvement
        };
    }
    
    // Learn from deployment performance
    learnFromProduction(deploymentMetrics) {
        this.performanceHistory.push({
            codeChanges: deploymentMetrics.changes,
            performanceImpact: deploymentMetrics.performance,
            userSatisfaction: deploymentMetrics.userMetrics
        });
        
        // Retrain model dengan new data
        this.retrainOptimizationModel();
    }
}

// ML-powered product recommendations
class PersonalizationEngine {
    constructor() {
        this.userProfile = {};
        this.recommendationModel = null;
    }
    
    async generateRecommendations(userId, context) {
        const userFeatures = await this.getUserFeatures(userId);
        const contextFeatures = this.getContextFeatures(context);
        
        const recommendations = await this.recommendationModel.predict({
            user: userFeatures,
            context: contextFeatures,
            inventory: await this.getAvailableProducts()
        });
        
        return recommendations.map(item => ({
            product: item,
            reason: this.explainRecommendation(item, userFeatures),
            confidence: item.mlScore
        }));
    }
    
    // A/B testing dengan ML optimization
    async optimizeRecommendationStrategy() {
        const strategies = ['collaborative', 'content-based', 'hybrid'];
        const results = await Promise.all(
            strategies.map(strategy => this.testStrategy(strategy))
        );
        
        // Use ML untuk determine optimal strategy mix
        const optimalMix = await this.optimizationModel.findBestCombination(results);
        return optimalMix;
    }
}

// ML-powered performance monitoring
class IntelligentPerformanceMonitor {
    constructor() {
        this.performanceData = [];
        this.anomalyDetectionModel = null;
        this.optimizationModel = null;
    }
    
    // Detect performance anomalies
    async detectPerformanceIssues(metrics) {
        const anomalyScore = await this.anomalyDetectionModel.predict(metrics);
        
        if (anomalyScore > 0.8) {
            return {
                isAnomaly: true,
                severity: this.calculateSeverity(anomalyScore),
                suggestedActions: await this.getSuggestedOptimizations(metrics)
            };
        }
        
        return { isAnomaly: false };
    }
    
    // Predictive scaling
    async predictResourceNeeds(timeHorizon = '1h') {
        const historicalData = this.getHistoricalMetrics();
        const prediction = await this.forecastingModel.predict({
            historical: historicalData,
            timeHorizon: timeHorizon,
            externalFactors: await this.getExternalFactors()
        });
        
        return {
            predictedLoad: prediction.load,
            recommendedScaling: prediction.scaling,
            confidence: prediction.confidence
        };
    }
}